




"""simple docstring"""




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     List[Any]					=				{
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"blip_2_vision_model"





				def __init__(			self						,					_a=1_408						,					_a=6_144						,					_a=39						,					_a=16						,					_a=224						,					_a=14						,					_a="gelu"						,					_a=0.00_001						,					_a=0.0						,					_a=1e-1_0						,					_a=True						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a					)

							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   patch_size
							lowerCamelCase      =   image_size
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   attention_dropout
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   qkv_bias





				@classmethod
				def   _lowerCAmelCase							(			cls						,					_a						,					**_a					):





							"""simple docstring"""
							cls._set_token_in_kwargs(_a					)

							lowerCamelCase ,  lowerCamelCase      =   cls.get_config_dict(_a						,					**_a					)

							# get the vision config dict if we are loading from Blip2Config
							if config_dict.get("""model_type"""					) == "blip-2":
										lowerCamelCase      =   config_dict["""vision_config"""]

							if "model_type" in config_dict and hasattr(cls						,					"""model_type"""					) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
										    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

							return cls.from_dict(_a						,					**_a					)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"blip_2_qformer"





				def __init__(			self						,					_a=30_522						,					_a=768						,					_a=12						,					_a=12						,					_a=3_072						,					_a="gelu"						,					_a=0.1						,					_a=0.1						,					_a=512						,					_a=0.02						,					_a=1e-1_2						,					_a=0						,					_a="absolute"						,					_a=2						,					_a=1_408						,					**_a						,					):





							"""simple docstring"""
							super().__init__(pad_token_id=_a						,					**_a					)

							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   position_embedding_type
							lowerCamelCase      =   cross_attention_frequency
							lowerCamelCase      =   encoder_hidden_size





				@classmethod
				def   _lowerCAmelCase							(			cls						,					_a						,					**_a					):





							"""simple docstring"""
							cls._set_token_in_kwargs(_a					)

							lowerCamelCase ,  lowerCamelCase      =   cls.get_config_dict(_a						,					**_a					)

							# get the qformer config dict if we are loading from Blip2Config
							if config_dict.get("""model_type"""					) == "blip-2":
										lowerCamelCase      =   config_dict["""qformer_config"""]

							if "model_type" in config_dict and hasattr(cls						,					"""model_type"""					) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
										    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

							return cls.from_dict(_a						,					**_a					)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"blip-2"
				__UpperCamelCase         =		True





				def __init__(			self						,					_a=None						,					_a=None						,					_a=None						,					_a=32						,					**_a					):





							"""simple docstring"""
							super().__init__(**_a					)

							if vision_config is None:
										lowerCamelCase      =   {}
										logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""					)

							if qformer_config is None:
										lowerCamelCase      =   {}
										logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""					)

							if text_config is None:
										lowerCamelCase      =   {}
										logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""					)

							lowerCamelCase      =   BlipaVisionConfig(**_a					)
							lowerCamelCase      =   BlipaQFormerConfig(**_a					)
							lowerCamelCase      =   text_config["""model_type"""] if """model_type""" in text_config else """opt"""
							lowerCamelCase      =   CONFIG_MAPPING[text_model_type](**_a					)

							lowerCamelCase      =   self.text_config.tie_word_embeddings
							lowerCamelCase      =   self.text_config.is_encoder_decoder

							lowerCamelCase      =   num_query_tokens
							lowerCamelCase      =   self.vision_config.hidden_size
							lowerCamelCase      =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							lowerCamelCase      =   1.0
							lowerCamelCase      =   0.02





				@classmethod
				def   _lowerCAmelCase							(			cls						,					_a						,					_a						,					_a						,					**_a						,					):





							"""simple docstring"""
							return cls(
							    vision_config=vision_config.to_dict()						,					qformer_config=qformer_config.to_dict()						,					text_config=text_config.to_dict()						,					**_a						,					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   copy.deepcopy(self.__dict__					)
							lowerCamelCase      =   self.vision_config.to_dict()
							lowerCamelCase      =   self.qformer_config.to_dict()
							lowerCamelCase      =   self.text_config.to_dict()
							lowerCamelCase      =   self.__class__.model_type
							return output









"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



